Numerous schemes have been proposed to predict the movement of objects relative to one another for navigational and other purposes. Acoustic echoing, for instance, provides an established method of determining the range from one object to another. If one object is approaching another, such ranging measurements can be used to estimate a time-to-contact.
In an environment where optical images of an object are available, a number of techniques have been proposed to model common biological correlation mechanisms for sensing image expansion, rotation, and translation. In such techniques, portions of an image at a first instant of time are compared with portions of the image at a second instant of time, to determine an optical "flow" over time. Correlation techniques are typically used to predict the actual flow, and other established algorithms are used to determine whether the flow suggests a change in distance to the object, translational movement relative to the object, or rotational movement of the object.
Use of such models to predict when an observer and another object will contact one another is particularly useful. Industrial robots, intelligent highway vehicles, and numerous other devices could be improved by being able to predict contact with other objects. U.S. Pat. No. 5,128,874 teaches an obstacle detection system using a combination of binocular stereo vision and laser radar ranging. U.S. Pat. No. 4,905,151 teaches a mobile robot vision system in which a one-dimensional image is processed in conjunction with a priori information about robot velocity to yield optical flow information used to determine distance to an object.
One technique for implementing movement detectors is discussed in Poggio et al., Green Theorems and Qualitative Properties of the Optical Flow, MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY MEMO NO. 1289, MASSACHUSETTS INSTITUTE OF TECHNOLOGY CENTER FOR BIOLOGICAL INFORMATION PROCESSING PAPER NO. 67 (April 1991), incorporated herein by reference. In this technique, algorithms for correlating optical flow elements and integrating the optical flow over a two-dimensional image plane provide data from which the amount of image expansion or rotation may be determined. This technique uses a two-dimensional correlation of two-dimensional image portions, or "patches," to obtain the desired results.
While such a scheme is theoretically robust, two-dimensional correlation of two-dimensional image data is a complex computational task, requiring sophisticated and expensive equipment for real-time results. Thus, with the present state-of-the-art technology, it is found that such a scheme is impractical for use in many desired applications.
Ideally, a simplified technique for estimating image expansion, translation or rotation would require less computational complexity and would be more amenable to practical application. None of the known teachings, however, provides such a technique.